Overview

Dataset statistics

Number of variables13
Number of observations2968
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory301.6 KiB
Average record size in memory104.0 B

Variable types

Numeric13

Alerts

gross_revenue is highly correlated with qtde_invoices and 2 other fieldsHigh correlation
recency_days is highly correlated with qtde_invoicesHigh correlation
qtde_invoices is highly correlated with gross_revenue and 4 other fieldsHigh correlation
qtde_items is highly correlated with gross_revenue and 2 other fieldsHigh correlation
qtde_products is highly correlated with gross_revenue and 3 other fieldsHigh correlation
avg_ticket is highly correlated with avg_unique_basket_sizeHigh correlation
avg_recency_days is highly correlated with frequencyHigh correlation
frequency is highly correlated with avg_recency_daysHigh correlation
avg_basket_size is highly correlated with qtde_invoices and 1 other fieldsHigh correlation
avg_unique_basket_size is highly correlated with qtde_products and 2 other fieldsHigh correlation
gross_revenue is highly correlated with qtde_invoices and 1 other fieldsHigh correlation
qtde_invoices is highly correlated with gross_revenue and 2 other fieldsHigh correlation
qtde_items is highly correlated with gross_revenue and 1 other fieldsHigh correlation
qtde_products is highly correlated with qtde_invoicesHigh correlation
avg_ticket is highly correlated with qtde_retrunsHigh correlation
qtde_retruns is highly correlated with avg_ticketHigh correlation
avg_basket_size is highly correlated with avg_unique_basket_sizeHigh correlation
avg_unique_basket_size is highly correlated with avg_basket_sizeHigh correlation
gross_revenue is highly correlated with qtde_invoices and 2 other fieldsHigh correlation
qtde_invoices is highly correlated with gross_revenue and 3 other fieldsHigh correlation
qtde_items is highly correlated with gross_revenue and 2 other fieldsHigh correlation
qtde_products is highly correlated with gross_revenue and 2 other fieldsHigh correlation
avg_recency_days is highly correlated with frequencyHigh correlation
frequency is highly correlated with avg_recency_daysHigh correlation
avg_basket_size is highly correlated with qtde_invoicesHigh correlation
gross_revenue is highly correlated with qtde_invoices and 3 other fieldsHigh correlation
qtde_invoices is highly correlated with gross_revenue and 3 other fieldsHigh correlation
qtde_items is highly correlated with gross_revenue and 3 other fieldsHigh correlation
qtde_products is highly correlated with gross_revenue and 3 other fieldsHigh correlation
avg_ticket is highly correlated with qtde_retrunsHigh correlation
qtde_retruns is highly correlated with gross_revenue and 4 other fieldsHigh correlation
avg_basket_size is highly correlated with avg_unique_basket_sizeHigh correlation
avg_unique_basket_size is highly correlated with avg_basket_sizeHigh correlation
avg_ticket is highly skewed (γ1 = 25.1569664) Skewed
frequency is highly skewed (γ1 = 24.87687084) Skewed
qtde_retruns is highly skewed (γ1 = 21.9754032) Skewed
df_index has unique values Unique
customer_id has unique values Unique
recency_days has 33 (1.1%) zeros Zeros
qtde_retruns has 1481 (49.9%) zeros Zeros

Reproduction

Analysis started2021-11-20 18:03:21.947122
Analysis finished2021-11-20 18:03:53.470894
Duration31.52 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

df_index
Real number (ℝ≥0)

UNIQUE

Distinct2968
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2316.666442
Minimum0
Maximum5714
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2021-11-20T15:03:53.580620image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile185.35
Q1928.5
median2119.5
Q33536.25
95-th percentile5034.3
Maximum5714
Range5714
Interquartile range (IQR)2607.75

Descriptive statistics

Standard deviation1554.722712
Coefficient of variation (CV)0.6711033938
Kurtosis-1.010637904
Mean2316.666442
Median Absolute Deviation (MAD)1270.5
Skewness0.3426249769
Sum6875866
Variance2417162.71
MonotonicityStrictly increasing
2021-11-20T15:03:53.745111image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01
 
< 0.1%
30101
 
< 0.1%
29951
 
< 0.1%
29961
 
< 0.1%
29991
 
< 0.1%
30001
 
< 0.1%
30011
 
< 0.1%
30021
 
< 0.1%
30051
 
< 0.1%
30071
 
< 0.1%
Other values (2958)2958
99.7%
ValueCountFrequency (%)
01
< 0.1%
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
51
< 0.1%
61
< 0.1%
71
< 0.1%
81
< 0.1%
91
< 0.1%
ValueCountFrequency (%)
57141
< 0.1%
56951
< 0.1%
56851
< 0.1%
56791
< 0.1%
56581
< 0.1%
56541
< 0.1%
56481
< 0.1%
56371
< 0.1%
56361
< 0.1%
56261
< 0.1%

customer_id
Real number (ℝ≥0)

UNIQUE

Distinct2968
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15270.37702
Minimum12347
Maximum18287
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2021-11-20T15:03:53.921037image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum12347
5-th percentile12619.35
Q113798.75
median15220.5
Q316768.5
95-th percentile17964.65
Maximum18287
Range5940
Interquartile range (IQR)2969.75

Descriptive statistics

Standard deviation1719.144523
Coefficient of variation (CV)0.1125803587
Kurtosis-1.206178196
Mean15270.37702
Median Absolute Deviation (MAD)1489
Skewness0.03219371129
Sum45322479
Variance2955457.892
MonotonicityNot monotonic
2021-11-20T15:03:54.079198image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
178501
 
< 0.1%
126701
 
< 0.1%
177341
 
< 0.1%
149051
 
< 0.1%
161031
 
< 0.1%
146261
 
< 0.1%
148681
 
< 0.1%
182461
 
< 0.1%
171151
 
< 0.1%
166111
 
< 0.1%
Other values (2958)2958
99.7%
ValueCountFrequency (%)
123471
< 0.1%
123481
< 0.1%
123521
< 0.1%
123561
< 0.1%
123581
< 0.1%
123591
< 0.1%
123601
< 0.1%
123621
< 0.1%
123641
< 0.1%
123701
< 0.1%
ValueCountFrequency (%)
182871
< 0.1%
182831
< 0.1%
182821
< 0.1%
182771
< 0.1%
182761
< 0.1%
182741
< 0.1%
182731
< 0.1%
182721
< 0.1%
182701
< 0.1%
182691
< 0.1%

gross_revenue
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2953
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2693.485061
Minimum6.2
Maximum279138.02
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2021-11-20T15:03:54.245054image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum6.2
5-th percentile229.7325
Q1570.845
median1085.51
Q32306.905
95-th percentile7169.562
Maximum279138.02
Range279131.82
Interquartile range (IQR)1736.06

Descriptive statistics

Standard deviation10135.46528
Coefficient of variation (CV)3.762955818
Kurtosis397.3013221
Mean2693.485061
Median Absolute Deviation (MAD)670.84
Skewness17.63537227
Sum7994263.66
Variance102727656.5
MonotonicityNot monotonic
2021-11-20T15:03:54.412627image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1078.962
 
0.1%
2053.022
 
0.1%
3312
 
0.1%
1353.742
 
0.1%
889.932
 
0.1%
745.062
 
0.1%
379.652
 
0.1%
2092.322
 
0.1%
731.92
 
0.1%
734.942
 
0.1%
Other values (2943)2948
99.3%
ValueCountFrequency (%)
6.21
< 0.1%
13.31
< 0.1%
151
< 0.1%
36.561
< 0.1%
451
< 0.1%
521
< 0.1%
52.21
< 0.1%
52.21
< 0.1%
62.431
< 0.1%
68.841
< 0.1%
ValueCountFrequency (%)
279138.021
< 0.1%
259657.31
< 0.1%
194550.791
< 0.1%
140450.721
< 0.1%
124564.531
< 0.1%
117379.631
< 0.1%
91062.381
< 0.1%
72882.091
< 0.1%
66653.561
< 0.1%
65039.621
< 0.1%

recency_days
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct272
Distinct (%)9.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64.30929919
Minimum0
Maximum373
Zeros33
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2021-11-20T15:03:54.610304image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q111
median31
Q381
95-th percentile242
Maximum373
Range373
Interquartile range (IQR)70

Descriptive statistics

Standard deviation77.76092244
Coefficient of variation (CV)1.209170733
Kurtosis2.776517247
Mean64.30929919
Median Absolute Deviation (MAD)26
Skewness1.798052889
Sum190870
Variance6046.761059
MonotonicityNot monotonic
2021-11-20T15:03:54.825794image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
199
 
3.3%
487
 
2.9%
285
 
2.9%
385
 
2.9%
876
 
2.6%
1067
 
2.3%
966
 
2.2%
766
 
2.2%
1764
 
2.2%
1655
 
1.9%
Other values (262)2218
74.7%
ValueCountFrequency (%)
033
 
1.1%
199
3.3%
285
2.9%
385
2.9%
487
2.9%
543
1.4%
766
2.2%
876
2.6%
966
2.2%
1067
2.3%
ValueCountFrequency (%)
3732
0.1%
3724
0.1%
3711
 
< 0.1%
3681
 
< 0.1%
3664
0.1%
3652
0.1%
3641
 
< 0.1%
3601
 
< 0.1%
3591
 
< 0.1%
3584
0.1%

qtde_invoices
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct56
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.724393531
Minimum1
Maximum206
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2021-11-20T15:03:54.995873image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q36
95-th percentile17
Maximum206
Range205
Interquartile range (IQR)4

Descriptive statistics

Standard deviation8.857759893
Coefficient of variation (CV)1.547370886
Kurtosis190.7862392
Mean5.724393531
Median Absolute Deviation (MAD)2
Skewness10.76555481
Sum16990
Variance78.45991032
MonotonicityNot monotonic
2021-11-20T15:03:55.170681image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2784
26.4%
3499
16.8%
4393
13.2%
5237
 
8.0%
1190
 
6.4%
6173
 
5.8%
7138
 
4.6%
898
 
3.3%
969
 
2.3%
1055
 
1.9%
Other values (46)332
11.2%
ValueCountFrequency (%)
1190
 
6.4%
2784
26.4%
3499
16.8%
4393
13.2%
5237
 
8.0%
6173
 
5.8%
7138
 
4.6%
898
 
3.3%
969
 
2.3%
1055
 
1.9%
ValueCountFrequency (%)
2061
< 0.1%
1991
< 0.1%
1241
< 0.1%
971
< 0.1%
912
0.1%
861
< 0.1%
721
< 0.1%
622
0.1%
601
< 0.1%
571
< 0.1%

qtde_items
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1670
Distinct (%)56.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1582.104447
Minimum1
Maximum196844
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2021-11-20T15:03:55.326499image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile102.35
Q1296
median640
Q31399.5
95-th percentile4403.25
Maximum196844
Range196843
Interquartile range (IQR)1103.5

Descriptive statistics

Standard deviation5705.291445
Coefficient of variation (CV)3.60614083
Kurtosis516.7418024
Mean1582.104447
Median Absolute Deviation (MAD)421
Skewness18.73765362
Sum4695686
Variance32550350.48
MonotonicityNot monotonic
2021-11-20T15:03:55.527274image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
31011
 
0.4%
1509
 
0.3%
889
 
0.3%
2468
 
0.3%
2728
 
0.3%
848
 
0.3%
2608
 
0.3%
2888
 
0.3%
12007
 
0.2%
5167
 
0.2%
Other values (1660)2885
97.2%
ValueCountFrequency (%)
11
< 0.1%
22
0.1%
122
0.1%
161
< 0.1%
171
< 0.1%
181
< 0.1%
191
< 0.1%
201
< 0.1%
231
< 0.1%
251
< 0.1%
ValueCountFrequency (%)
1968441
< 0.1%
802631
< 0.1%
773731
< 0.1%
699931
< 0.1%
645491
< 0.1%
641241
< 0.1%
633121
< 0.1%
583431
< 0.1%
578851
< 0.1%
502551
< 0.1%

qtde_products
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct468
Distinct (%)15.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean122.7644879
Minimum1
Maximum7838
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2021-11-20T15:03:55.701548image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile9
Q129
median67
Q3135
95-th percentile382
Maximum7838
Range7837
Interquartile range (IQR)106

Descriptive statistics

Standard deviation269.9329358
Coefficient of variation (CV)2.198786803
Kurtosis354.7788412
Mean122.7644879
Median Absolute Deviation (MAD)44
Skewness15.7061352
Sum364365
Variance72863.78981
MonotonicityNot monotonic
2021-11-20T15:03:55.859362image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2843
 
1.4%
2037
 
1.2%
3535
 
1.2%
2935
 
1.2%
1934
 
1.1%
1533
 
1.1%
1132
 
1.1%
2631
 
1.0%
2730
 
1.0%
2530
 
1.0%
Other values (458)2628
88.5%
ValueCountFrequency (%)
16
 
0.2%
214
0.5%
315
0.5%
417
0.6%
526
0.9%
629
1.0%
718
0.6%
819
0.6%
926
0.9%
1028
0.9%
ValueCountFrequency (%)
78381
< 0.1%
56731
< 0.1%
50951
< 0.1%
45801
< 0.1%
26981
< 0.1%
23791
< 0.1%
20601
< 0.1%
18181
< 0.1%
16731
< 0.1%
16371
< 0.1%

avg_ticket
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED

Distinct2965
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.99425671
Minimum2.150588235
Maximum4453.43
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2021-11-20T15:03:56.095494image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum2.150588235
5-th percentile4.915887985
Q113.11811111
median17.95344712
Q324.98179365
95-th percentile90.052125
Maximum4453.43
Range4451.279412
Interquartile range (IQR)11.86368254

Descriptive statistics

Standard deviation119.5320656
Coefficient of variation (CV)3.622814318
Kurtosis812.9647397
Mean32.99425671
Median Absolute Deviation (MAD)5.979018644
Skewness25.1569664
Sum97926.95393
Variance14287.91471
MonotonicityNot monotonic
2021-11-20T15:03:56.252903image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
152
 
0.1%
4.1622
 
0.1%
14.478333332
 
0.1%
18.152222221
 
< 0.1%
13.927368421
 
< 0.1%
36.244117651
 
< 0.1%
29.784166671
 
< 0.1%
22.87926231
 
< 0.1%
20.511041671
 
< 0.1%
149.0251
 
< 0.1%
Other values (2955)2955
99.6%
ValueCountFrequency (%)
2.1505882351
< 0.1%
2.43251
< 0.1%
2.4623711341
< 0.1%
2.5112413791
< 0.1%
2.5153333331
< 0.1%
2.651
< 0.1%
2.6569318181
< 0.1%
2.7075982531
< 0.1%
2.7606215721
< 0.1%
2.7704641911
< 0.1%
ValueCountFrequency (%)
4453.431
< 0.1%
3202.921
< 0.1%
1687.21
< 0.1%
952.98751
< 0.1%
872.131
< 0.1%
841.02144931
< 0.1%
651.16833331
< 0.1%
6401
< 0.1%
624.41
< 0.1%
615.751
< 0.1%

avg_recency_days
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct1258
Distinct (%)42.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean67.30213285
Minimum1
Maximum366
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2021-11-20T15:03:56.418163image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile8
Q125.91730769
median48.26785714
Q385.33333333
95-th percentile200.65
Maximum366
Range365
Interquartile range (IQR)59.41602564

Descriptive statistics

Standard deviation63.50535844
Coefficient of variation (CV)0.9435861206
Kurtosis4.908048776
Mean67.30213285
Median Absolute Deviation (MAD)26.26785714
Skewness2.066084007
Sum199752.7303
Variance4032.93055
MonotonicityNot monotonic
2021-11-20T15:03:56.576465image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1425
 
0.8%
422
 
0.7%
7021
 
0.7%
720
 
0.7%
3519
 
0.6%
4918
 
0.6%
1117
 
0.6%
4617
 
0.6%
2117
 
0.6%
2816
 
0.5%
Other values (1248)2776
93.5%
ValueCountFrequency (%)
116
0.5%
1.51
 
< 0.1%
213
0.4%
2.51
 
< 0.1%
2.6013986011
 
< 0.1%
315
0.5%
3.3214285711
 
< 0.1%
3.3303571431
 
< 0.1%
3.52
 
0.1%
422
0.7%
ValueCountFrequency (%)
3661
 
< 0.1%
3651
 
< 0.1%
3631
 
< 0.1%
3621
 
< 0.1%
3572
0.1%
3561
 
< 0.1%
3552
0.1%
3521
 
< 0.1%
3512
0.1%
3503
0.1%

frequency
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
SKEWED

Distinct1225
Distinct (%)41.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1138323742
Minimum0.005449591281
Maximum17
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2021-11-20T15:03:56.751055image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.005449591281
5-th percentile0.008893504781
Q10.01633986928
median0.02589835169
Q30.04947858264
95-th percentile1
Maximum17
Range16.99455041
Interquartile range (IQR)0.03313871336

Descriptive statistics

Standard deviation0.4082205551
Coefficient of variation (CV)3.586155151
Kurtosis989.0663249
Mean0.1138323742
Median Absolute Deviation (MAD)0.0121968864
Skewness24.87687084
Sum337.8544866
Variance0.1666440216
MonotonicityNot monotonic
2021-11-20T15:03:56.927849image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1198
 
6.7%
0.062518
 
0.6%
0.0277777777817
 
0.6%
0.0238095238116
 
0.5%
0.0909090909115
 
0.5%
0.0833333333315
 
0.5%
0.0344827586214
 
0.5%
0.0294117647114
 
0.5%
0.0357142857113
 
0.4%
0.0769230769213
 
0.4%
Other values (1215)2635
88.8%
ValueCountFrequency (%)
0.0054495912811
 
< 0.1%
0.0054644808741
 
< 0.1%
0.0054794520551
 
< 0.1%
0.0054945054951
 
< 0.1%
0.0055865921792
0.1%
0.0056022408961
 
< 0.1%
0.0056179775282
0.1%
0.005665722381
 
< 0.1%
0.0056818181822
0.1%
0.0056980056983
0.1%
ValueCountFrequency (%)
171
 
< 0.1%
31
 
< 0.1%
26
 
0.2%
1.1428571431
 
< 0.1%
1198
6.7%
0.751
 
< 0.1%
0.66666666673
 
0.1%
0.5508021391
 
< 0.1%
0.53351206431
 
< 0.1%
0.53
 
0.1%

qtde_retruns
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct213
Distinct (%)7.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.88847709
Minimum0
Maximum9014
Zeros1481
Zeros (%)49.9%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2021-11-20T15:03:57.131327image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q39
95-th percentile100
Maximum9014
Range9014
Interquartile range (IQR)9

Descriptive statistics

Standard deviation282.864784
Coefficient of variation (CV)8.107685048
Kurtosis596.2019916
Mean34.88847709
Median Absolute Deviation (MAD)1
Skewness21.9754032
Sum103549
Variance80012.48604
MonotonicityNot monotonic
2021-11-20T15:03:57.311180image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01481
49.9%
1164
 
5.5%
2148
 
5.0%
3105
 
3.5%
489
 
3.0%
678
 
2.6%
561
 
2.1%
1251
 
1.7%
743
 
1.4%
843
 
1.4%
Other values (203)705
23.8%
ValueCountFrequency (%)
01481
49.9%
1164
 
5.5%
2148
 
5.0%
3105
 
3.5%
489
 
3.0%
561
 
2.1%
678
 
2.6%
743
 
1.4%
843
 
1.4%
941
 
1.4%
ValueCountFrequency (%)
90141
< 0.1%
80041
< 0.1%
44271
< 0.1%
37681
< 0.1%
33321
< 0.1%
28781
< 0.1%
20221
< 0.1%
20121
< 0.1%
17761
< 0.1%
15941
< 0.1%

avg_basket_size
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct268
Distinct (%)9.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.118996421
Minimum0.1764705882
Maximum16
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2021-11-20T15:03:57.482976image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.1764705882
5-th percentile0.95
Q11.8
median2.75
Q34
95-th percentile6.5
Maximum16
Range15.82352941
Interquartile range (IQR)2.2

Descriptive statistics

Standard deviation1.833650164
Coefficient of variation (CV)0.5878974888
Kurtosis3.642014851
Mean3.118996421
Median Absolute Deviation (MAD)1.083333333
Skewness1.430768983
Sum9257.181378
Variance3.362272923
MonotonicityNot monotonic
2021-11-20T15:03:57.693252image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3204
 
6.9%
2200
 
6.7%
4163
 
5.5%
5142
 
4.8%
3.5136
 
4.6%
4.5113
 
3.8%
2.5111
 
3.7%
682
 
2.8%
3.33333333371
 
2.4%
170
 
2.4%
Other values (258)1676
56.5%
ValueCountFrequency (%)
0.17647058821
 
< 0.1%
0.22110552761
 
< 0.1%
0.27272727271
 
< 0.1%
0.27669902911
 
< 0.1%
0.27906976741
 
< 0.1%
0.28205128211
 
< 0.1%
0.33064516131
 
< 0.1%
0.33333333334
0.1%
0.34020618561
 
< 0.1%
0.36263736261
 
< 0.1%
ValueCountFrequency (%)
161
 
< 0.1%
143
 
0.1%
13.51
 
< 0.1%
121
 
< 0.1%
119
 
0.3%
107
 
0.2%
9.51
 
< 0.1%
916
0.5%
8.52
 
0.1%
834
1.1%

avg_unique_basket_size
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct906
Distinct (%)30.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.48997702
Minimum0.2
Maximum259
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2021-11-20T15:03:57.888523image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.2
5-th percentile2
Q17.666666667
median13.6
Q322.14464286
95-th percentile46
Maximum259
Range258.8
Interquartile range (IQR)14.47797619

Descriptive statistics

Standard deviation15.46012684
Coefficient of variation (CV)0.8839420902
Kurtosis29.32468467
Mean17.48997702
Median Absolute Deviation (MAD)6.6
Skewness3.436467798
Sum51910.25179
Variance239.015522
MonotonicityNot monotonic
2021-11-20T15:03:58.050277image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1342
 
1.4%
941
 
1.4%
839
 
1.3%
1639
 
1.3%
1738
 
1.3%
1438
 
1.3%
1136
 
1.2%
536
 
1.2%
736
 
1.2%
1535
 
1.2%
Other values (896)2588
87.2%
ValueCountFrequency (%)
0.21
 
< 0.1%
0.253
 
0.1%
0.33333333336
0.2%
0.41
 
< 0.1%
0.40909090911
 
< 0.1%
0.512
0.4%
0.54545454551
 
< 0.1%
0.55555555561
 
< 0.1%
0.57142857141
 
< 0.1%
0.61764705881
 
< 0.1%
ValueCountFrequency (%)
2591
< 0.1%
1771
< 0.1%
1481
< 0.1%
1271
< 0.1%
1051
< 0.1%
1041
< 0.1%
1011
< 0.1%
981
< 0.1%
95.51
< 0.1%
94.333333331
< 0.1%

Interactions

2021-11-20T15:03:50.881127image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-20T15:03:27.223231image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-20T15:03:29.437201image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-20T15:03:31.342389image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-20T15:03:33.290874image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-20T15:03:35.331539image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-20T15:03:37.097582image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-20T15:03:39.199032image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-20T15:03:41.269388image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-20T15:03:43.075691image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-20T15:03:44.987854image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-20T15:03:47.026895image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-20T15:03:48.870564image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-20T15:03:51.011328image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-20T15:03:27.605984image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-20T15:03:29.577854image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-20T15:03:31.501148image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-20T15:03:33.437192image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-20T15:03:35.461494image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-20T15:03:37.250760image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-20T15:03:39.349302image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-20T15:03:41.387882image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-20T15:03:43.211916image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-20T15:03:45.154870image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-20T15:03:47.153522image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-20T15:03:49.026459image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-20T15:03:51.141495image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-20T15:03:27.793404image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-20T15:03:29.712275image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-20T15:03:31.628405image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-20T15:03:33.606402image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-20T15:03:35.611327image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-20T15:03:37.419044image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-20T15:03:39.496097image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-20T15:03:41.527185image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-20T15:03:43.381134image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-20T15:03:45.325910image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-20T15:03:47.341035image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-20T15:03:49.182795image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-20T15:03:51.316903image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-20T15:03:27.924987image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-20T15:03:29.848959image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-20T15:03:31.765806image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-20T15:03:33.782988image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-20T15:03:35.746696image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-20T15:03:37.556743image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-20T15:03:39.632782image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-20T15:03:41.666807image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-20T15:03:43.510342image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-20T15:03:45.472574image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-20T15:03:47.468864image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-20T15:03:49.383585image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-20T15:03:51.465930image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-20T15:03:28.089972image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-20T15:03:30.000609image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-20T15:03:31.908226image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-20T15:03:33.932584image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-20T15:03:35.878274image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-20T15:03:37.696105image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-20T15:03:39.774586image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-20T15:03:41.793535image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-20T15:03:43.669353image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-20T15:03:45.638987image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-20T15:03:47.604103image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-20T15:03:49.533371image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-20T15:03:51.601301image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-20T15:03:28.217713image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-20T15:03:30.141032image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-20T15:03:32.081196image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-20T15:03:34.066271image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-20T15:03:36.006937image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-20T15:03:37.859262image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-20T15:03:39.959896image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-20T15:03:41.920858image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-20T15:03:43.810058image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-20T15:03:45.779395image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-20T15:03:47.725796image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-20T15:03:49.667588image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-20T15:03:51.756684image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-20T15:03:28.366514image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-20T15:03:30.283468image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-20T15:03:32.226824image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-20T15:03:34.249065image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-20T15:03:36.144447image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-20T15:03:38.063487image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-20T15:03:40.158632image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-20T15:03:42.065303image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-20T15:03:43.972111image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-20T15:03:45.927960image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-20T15:03:47.857332image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-20T15:03:49.829978image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-20T15:03:51.916803image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-20T15:03:28.522931image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-20T15:03:30.455940image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-20T15:03:32.395924image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-20T15:03:34.396229image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-20T15:03:36.279833image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-20T15:03:38.237670image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-20T15:03:40.306900image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-20T15:03:42.233262image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-20T15:03:44.121691image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-20T15:03:46.087976image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-20T15:03:48.002270image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-20T15:03:49.991355image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-20T15:03:52.046936image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-20T15:03:28.656694image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-20T15:03:30.586910image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-20T15:03:32.525502image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-20T15:03:34.567350image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-20T15:03:36.417558image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-20T15:03:38.377767image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-20T15:03:40.436786image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-20T15:03:42.346084image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-20T15:03:44.257483image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-20T15:03:46.255526image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-20T15:03:48.134384image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-20T15:03:50.148368image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-20T15:03:52.180424image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-20T15:03:28.823565image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-20T15:03:30.729048image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-20T15:03:32.664286image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-20T15:03:34.725510image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-20T15:03:36.555701image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-20T15:03:38.563092image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-20T15:03:40.594641image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-20T15:03:42.483275image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-20T15:03:44.398039image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-20T15:03:46.399395image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-20T15:03:48.286553image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-20T15:03:50.289272image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-20T15:03:52.320570image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-20T15:03:28.985219image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-20T15:03:30.875894image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-20T15:03:32.810667image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-20T15:03:34.917029image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-20T15:03:36.687886image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-20T15:03:38.726509image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-20T15:03:40.813696image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-20T15:03:42.657699image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-20T15:03:44.536370image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-20T15:03:46.569054image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-20T15:03:48.424101image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-20T15:03:50.455792image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-20T15:03:52.455376image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-20T15:03:29.118834image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-20T15:03:31.013209image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-20T15:03:32.964813image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-20T15:03:35.050860image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-20T15:03:36.812544image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-20T15:03:38.877750image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-20T15:03:40.976864image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-20T15:03:42.772786image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-20T15:03:44.673522image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-20T15:03:46.732666image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-20T15:03:48.554683image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-20T15:03:50.601933image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-20T15:03:52.622278image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-20T15:03:29.257961image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-20T15:03:31.215262image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-20T15:03:33.156626image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-20T15:03:35.193047image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-20T15:03:36.958250image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-20T15:03:39.039419image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-20T15:03:41.132808image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-20T15:03:42.929077image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-20T15:03:44.832637image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-20T15:03:46.889339image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-20T15:03:48.719009image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-20T15:03:50.741894image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Correlations

2021-11-20T15:03:58.276049image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-11-20T15:03:58.525893image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-11-20T15:03:58.758004image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-11-20T15:03:58.999574image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-11-20T15:03:52.962722image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
A simple visualization of nullity by column.
2021-11-20T15:03:53.310452image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

df_indexcustomer_idgross_revenuerecency_daysqtde_invoicesqtde_itemsqtde_productsavg_ticketavg_recency_daysfrequencyqtde_retrunsavg_basket_sizeavg_unique_basket_size
00178505,391.2100372.000034.00001,733.0000297.000018.152235.500017.000040.00000.17650.6176
11130473,232.590056.00009.00001,390.0000171.000018.904027.25000.028335.00001.222211.6667
22125836,705.38002.000015.00005,028.0000232.000028.902523.18750.040350.00001.60007.6000
3313748948.250095.00005.0000439.000028.000033.866192.66670.01790.00001.60004.8000
4415100876.0000333.00003.000080.00003.0000292.00008.60000.073222.00000.66670.3333
55152914,623.300025.000014.00002,102.0000102.000045.326523.20000.040129.00001.21434.3571
66146885,630.87007.000021.00003,621.0000327.000017.219818.30000.0572399.00001.14297.0476
77178095,411.910016.000012.00002,057.000061.000088.719835.70000.033541.00001.91673.8333
881531160,767.90000.000091.000038,194.00002,379.000025.54354.14440.2433474.00000.47256.2308
99160982,005.630087.00007.0000613.000067.000029.934847.66670.02440.00002.14294.8571

Last rows

df_indexcustomer_idgross_revenuerecency_daysqtde_invoicesqtde_itemsqtde_productsavg_ticketavg_recency_daysfrequencyqtde_retrunsavg_basket_sizeavg_unique_basket_size
29585626177271,060.250015.00001.0000645.000066.000016.06446.00001.00006.000011.000066.0000
2959563617232421.52002.00002.0000203.000036.000011.708912.00000.15380.00005.000015.0000
2960563717468137.000010.00002.0000116.00005.000027.40004.00000.40000.00001.00002.5000
2961564813596697.04005.00002.0000406.0000166.00004.19907.00000.25000.00005.000066.5000
29625654148931,237.85009.00002.0000799.000073.000016.95682.00000.66670.00007.000036.0000
2963565812479473.200011.00001.0000382.000030.000015.77334.00001.000034.00008.000030.0000
2964567914126706.13007.00003.0000508.000015.000047.07533.00000.750050.00002.00004.6667
29655685135211,092.39001.00003.0000733.0000435.00002.51124.50000.30000.00003.0000104.0000
2966569515060301.84008.00004.0000262.0000120.00002.51531.00002.00000.00002.000020.0000
2967571412558269.96007.00001.0000196.000011.000024.54186.00001.0000196.00005.000011.0000